Sampling-based optimal kinodynamic planning with motion primitives

This paper proposes a novel sampling-based motion planner, which integrates in RRT* (Rapidly exploring Random Tree star) a database of pre-computed motion primitives to alleviate its computational load and allow for motion planning in a dynamic or partially known environment. The database is built by considering a set of initial and final state pairs in some grid space, and determining for each pair an optimal trajectory that is compatible with the system dynamics and constraints, while minimizing a cost. Nodes are progressively added to the tree {of feasible trajectories in the RRT* by extracting at random a sample in the gridded state space and selecting the best obstacle-free motion primitive in the database that joins it to an existing node. The tree is rewired if some nodes can be reached from the new sampled state through an obstacle-free motion primitive with lower cost. The computationally more intensive part of motion planning is thus moved to the preliminary offline phase of the database construction at the price of some performance degradation due to gridding. Grid resolution can be tuned so as to compromise between (sub)optimality and size of the database. The planner is shown to be asymptotically optimal as the grid resolution goes to zero and the number of sampled states grows to infinity

Bang-Bang Boosting of RRTs

This paper explores the use of time-optimal controls to improve the performance of sampling-based kinodynamic planners. A computationally efficient steering method is introduced that produces time-optimal trajectories between any states for a vector of double integrators. This method is applied in three ways: 1) to generate RRT edges that quickly solve the two-point boundary-value problems, 2) to produce an RRT (quasi)metric for more accurate Voronoi bias, and 3) to time-optimize a given collision-free trajectory. Experiments are performed for state spaces with up to 2000 dimensions, resulting in improved computed trajectories and orders of magnitude computation time improvements over using ordinary metrics and constant controls

A Mathematical Characterization of Minimally Sufficient Robot Brains

This paper addresses the lower limits of encoding and processing the information acquired through interactions between an internal system (robot algorithms or software) and an external system (robot body and its environment) in terms of action and observation histories. Both are modeled as transition systems. We want to know the weakest internal system that is sufficient for achieving passive (filtering) and active (planning) tasks. We introduce the notion of an information transition system for the internal system which is a transition system over a space of information states that reflect a robot's or other observer's perspective based on limited sensing, memory, computation, and actuation. An information transition system is viewed as a filter and a policy or plan is viewed as a function that labels the states of this information transition system. Regardless of whether internal systems are obtained by learning algorithms, planning algorithms, or human insight, we want to know the limits of feasibility for given robot hardware and tasks. We establish, in a general setting, that minimal information transition systems exist up to reasonable equivalence assumptions, and are unique under some general conditions. We then apply the theory to generate new insights into several problems, including optimal sensor fusion/filtering, solving basic planning tasks, and finding minimal representations for modeling a system given input-output relations.Comment: arXiv admin note: text overlap with arXiv:2212.0052

Model based Detection and 3D Localization of Planar Objects for Industrial Setups

In this work we present a method to detect and estimate the three-dimensional pose of planar and textureless objects placed randomly on a conveyor belt or inside a bin. The method is based on analysis of single 2D images acquired by a standard camera. The algorithm exploits a template matching method to recognize the objects. A set of pose hypotheses are then refined and, based on a gradient orientation scoring, the best object to be manipulated is selected. The method is flexible and can be used with different objects without changing parameters since it exploits a CAD model as input for template generation. We validated the method using synthetic images. An experimental setup has been also designed using a fixed standard camera to localize planar metal objects in various scenarios

AİLEVİ AKDENİZ ATEŞİ HASTALARINDA MEFV GEN MUTASYONLARI SIKLIĞININ İNCELENMESİ

Background: The aim of this study was to investigate the types and distribution of MEFV gene mutations in patients with Familial Mediterranean Fever (FMF) who were followed up in the Rheumatology outpatient clinics of Kartal Dr. Lütfi Kırdar City Hospital. Method: A total of 282 unrelated patients who were admitted to rheumatology outpatient clinics between 2020 and 2022, diagnosed with FMF according to Tel-Hashomer criteria or newly diagnosed during this period were included in the study. The data of the patients were retrospectively screened through the hospital database. MEFV gene mutations of the patients were identified and recorded. Results: As a result of the study, only 26.1% of the patients were found to carry homozygous mutations. There was no significant difference between mutation type and gender, age at diagnosis and symptoms at the time of diagnosis. There was no significant difference between the number of attacks in the last year and mutation type. The most common mutations found in FMF patients were M694V, R202Q, M680I, V726A, E148Q and K695R in order of frequency. Conclusion: The most common mutations found in patients with FMF are M694V, R202Q, M680I, V726A, E148Q and K695R. However, no correlation has been shown between mutations and clinical findings.  Amaç: Bu çalışmanın amacı Kartal Dr. Lütfi Kırdar Şehir Hastanesi Romatoloji polikliniklerinde takip edilen Ailevi Akdeniz Ateşi (FMF) tanısı almış hastalardaki MEFV gen mutasyon tipleri ve dağılımını incelemektir. Yöntem: Romatoloji polikliniklerine 2020-2022 yılları arasında başvuran, Tel-Hashomer kriterlerine göre FMF tanısı almış veya bu dönem içerisinde yeni tanı alan, birbiriyle akrabalık ilişkisi olmayan 282 hasta çalışmaya dahil edilmiştir. Hastaların verileri hastane veri tabanı üzerinden retrospektif olarak taranmıştır. Hastaların MEFV gen mutasyonları sistemden belirlenerek kaydedilmiştir. Bulgular: Çalışma sonucunda hastaların sadece %26,1’inin homozigot mutasyon taşımakta  olduğu görüldü. Mutasyon tipi ile cinsiyet, tanı yaşı ve tanı anındaki semptomlar açısından anlamlı bir farklılık gösterilememiştir. Hastaların son bir yıl içinde yaşadıkları atak sayısı ve mutasyon tipi arasında anlamlı farklılık yoktu. FMF tanılı hatalarda en sık rastlanılan mutasyonlar sıklık sırasına göre; M694V, R202Q, M680I, V726A, E148Q ve K695R’ dir. Sonuç: FMF tanılı hastalarda en sık rastlanılan mutasyonlar M694V, R202Q, M680I, V726A, E148Q ve K695R’dir. Ancak mutasyonlar ile klinik bulgular arasında ilişki gösterilememiştir

Coordinated lane change in autonomous driving: a computationally aware solution

This paper addresses the design of coordinated maneuvers in an autonomous driving set-up involving multiple vehicles. In particular, we consider a lane change problem where a vehicle has to merge in a platoon traveling in the adjacent lane of a two-lane one way road. We propose a cooperative solution that trades optimality for computational feasibility without simplifying the merging vehicle dynamics. The key idea is decoupling the problem into two phases: an online coordination phase where vehicles in the platoon create a gap where the merging vehicle can safely enter, and a merging phase, where the merging vehicle change lane by tracking a pre-computed optimal maneuver. A numerical case study shows the achieved trade off between performance degradation and reduction in computing time of the proposed solution

Complete path planning that simultaneously optimizes length and clearance

Abstract This paper considers a fundamental, optimal path planning problem that requires simultaneously minimizing path length and maximizing obstacle clearance. We show that in even simple planar settings with point and disc obstacles, the set of alternative solutions such that no one is clearly better than another (the set of Pareto-optimal solutions) is uncountably infinite. In spite of this difficulty, we introduce a complete, efficient algorithm that computes the Pareto front and a data structure that finitely represents the complete set of all Pareto- optimal paths. Particular optimal paths can then be selected from the computed data structure during execution, based on any additional conditions or considerations

Homotopy aware kinodynamic planning using RRT-based planners

This paper introduces a method for kinodynamic planning with homotopy class constraints, and proposes a homotopy class identifier that establishes a geometric relation between a trajectory and a union of convex partitions of the 2D robot workspace. The proposed identifier is shown to be invariant with respect to the trajectories that belong to the same homotopy class, in such a way that each class has its own unique signature. Furthermore, we show that the proposed homotopy class identifier can be easily incorporated in a RRT-based planner, without changing the planning algorithm, while restricting the solution trajectories to a designated homotopy class

Human-like path planning in the presence of landmarks

This work proposes a path planning algorithm for scenarios where the agent has to move strictly inside the space defined by signal emitting bases. Considering a base can emit within a limited area, it is necessary for the agent to be in the vicinity of at least one base at each point along the path in order to receive a signal. The algorithm starts with forming a specific network, based on the starting point such that only the bases which allow the described motion are included. A second step is based on RRT*, where each edge is created solving an optimal control problem that at the end provides a human-like path. Finally the best path is selected among all the ones that reach the goal region with the minimum cost